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\title{Image Classification with Deep Learning}
\author{
    Xinrarn Liu,
    Fatemeh Karimi,
    Deepak Rishi \&
    Chris Choi
}
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\section*{Description}
Image Classification is a common yet challenging problem. Our aim is to participate in the Digital Recognizer Kaggle Challenge \cite{DigitalRecognizerKaggle}, where one has to correctly classify the Modified National Institute of Standards and Technology (MNIST) dataset of handwritten numerical digits. This dataset is a classic within the Machine Learning community and has been extensively studied.

For our first approach we propose using a simple Feed-Forward Neural Network to form a baseline for comparison. We then plan on experimenting on different aspects of a Neural Network such as:

\begin{itemize}
    \item{Network Architecture}
    \item{Activation Functions}
    \item{Learning Methods}
\end{itemize}

to further improve and explore different configurations of a Neural Network that would best classify the MNIST dataset.


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